Upload
others
View
4
Download
0
Embed Size (px)
Citation preview
ASSESSMENT OF SURFACE WATER DYNAMICS USING MULTIPLE WATER
INDICES AROUND ADAMA WOREDA, ETHIOPIA
Manikandan Sathianarayanan 1, *
1 Department of Civil Engineering, National Taiwan University, Taiwan - [email protected]
Commission V, SS: Natural Resources Management
KEY WORDS: NDWI, WRI, MNDWI, Surface water
ABSTRACT:
Rapid change of Adama wereda during the last three decades has posed a serious threat to the existence of ecological systems,
specifically water bodies which play a crucial part in supporting life. Role of Satellite images in Remote Sensing could be more
important in investigation, monitoring dynamically and planning of natural surface water resources. Landsat-5(TM) & Landsat 8 (OLI)
has high spatial, temporal and multispectral resolution and therefore provides consistent and perfect data to detect changes in surface
changes of water bodies. In this paper, a study was conducted to detect the changes in water body extent during the period of 1984,2000
&2017 using various water indices such as namely Water Ratio Index (WRI), Normalized Difference Water Index (NDWI), Modified
Normalized Difference Water Index (MNDWI), supervised classification and wetness component of K-T transformation and the results
are Presented. NDWI has been adopted for this study as compared with other indices through ground survey. The results showed an
intense decreasing trend in the lakes of chelekleka ,kiroftu ,lake 1 and lake 3 of surface area in the period 1984–2017, especially
between 2000 and 2017 when the lake lost about 1.309 km2 (one third) of its surface area compared to the year 2000, which is
equivalent to 76%,18% ,0.03% and 96%. Interestingly koka lake has shown very erratic changes in its area coverage by losing almost
3.5 sq.km between 1984 and 2000 and then climbing back up by 14.8 sq.km in 2017. Percentage of increment was observed that 10.6 %
as compared with previous year.
1. INTRODUCTION
Assessment and monitoring the changes in environment using
remote sensing technology is extensively used in various
applications, such as land use/cover change (Salmon et al.,2013;
Demir et al.,2013), disaster monitoring (Volpi et al.,2013; Brisco
et al.,2013), forest and vegetation change (Kaliraj et al., 2012;
Markogianni et al.,2013), urban sprawl (Bagan et al.,2012; Raja
et al.,2013), and hydrology (Dronova et al.,2011; Zhu et
al.,2011). surface water will play an important role in human
survival and social development Ridd and Liu (1998). It is most
needful for humans, food crops, and ecosystems (Lu et al.,2011).
Collecting information about the spatial distribution of open
surface water is most important in various scientific disciplines,
such as the assessment of present and future water resources,
river dynamics, wetland inventory, climate models watershed
analysis, surface water survey and management, agriculture
suitability, flood mapping, and environment monitoring (Desmet
and Govers,1996; Zhou and Wu,2008; Du et al.,2012; Sun et
al.,2012). Recently developed remote sensing satellites with
different spatial, spectral and temporal resolution provide an
abundant data that become primary sources and being used for
detecting and extracting surface water and its changes in recent
decades (Xu,2006; Zhou et al.,2011; Tang et al.,2013; Li et
al.,2013; McFeeters,2013). Images from Landsat series was one
of the most widely used remote sensing data that adopted for
water body detection as well as changes over the periods (Moradi
et al.,2017). Landsat series satellite has different sensor such as
multispectral scanner (MSS) for Landsat -1 to Landsat -3,
Thematic mapper (TM)for Landsat -4 and Landsat-5 and
Enhanced Thematic mapper plus(ETM+) have been used for
many environmental applications specifically for water body
* Corresponding author
dynamics studies (moradi et al.,2017, Mcfeeters,1996; Xu,2006).
Latest era on Landsat series has begun with Operational Land
Imager(OLI) on board Landsat 8 has 12-bit pixel instead of 8-bit
pixel of ETM+ that gives higher quality and better signal to noise
ratio than ETM+ (Irons et al.,2012).
Several algorithm has been adopted for change detection studies
on water body especially on Landsat data that categorized in to
four main groups:1- classification and pattern recognition
methods it includes supervised (Tulbure et al.,2013) and
unsupervised methods (Ko et al.,2015).2-spectral un-mixing
(sethre et al.,2005).3-single band threshold (Klein et al.,2014)
and 4-spectral water index (Ji et al.,2009). Errors are more
common when single band methods chosen to extract water
features along with different cover types because of defined
threshold values are needed to extract water features(Du et
al.,2012).index based methods are more accurate than
classification methods it does not require any basic knowledge
(Li et al.,2013).Multi band methods comprises two or more
different reflective bands for improved surface water extraction
(Du et al.,2012).For instance, Normalized difference water
index(NDWI) has developed for extracting water feature from
satellite imagery.
In the past decade there are Different indices has lined up for
extracting water surface.one of the popular water index is
normalized difference water index(NDWI)(Mcfeeters,1996).
Modified normalized difference water index has been developed
to overcome the problem of water pixel mixed with buildup
features(Xu,2006). Automated water extraction index (AWEI)
was introduced to get better result which Landsat image has
shadow and dark surface. Water Ratio Index (WRI) is another
widely used water index by Shen, L and C. Li (2010). This study
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume IV-5, 2018 ISPRS TC V Mid-term Symposium “Geospatial Technology – Pixel to People”, 20–23 November 2018, Dehradun, India
This contribution has been peer-reviewed. The double-blind peer-review was conducted on the basis of the full paper. https://doi.org/10.5194/isprs-annals-IV-5-181-2018 | © Authors 2018. CC BY 4.0 License.
181
has, therefore, implemented NDWI among others to extract and
quantify the water surface area changes of ADAMA wereda and
its surrounding. In doing so, Landsat images of 1984 (TM)
2000(TM) & 2017(OLI) has been used. The main objective of
this project is to detect the surface water change detection
patterns (1984-2017) of Landsat multispectral images using
different water indices in Adama woreda and its surrounding.
2. STUDY AREA
The study area, Adama wereda have 100145.61-hectare area
coverage. It comprises Adama city, Wonji sugar factory and
town, Koka Lake, and other rural Kebeles. Adama wereda is
located in Oromia region, east Shoa zone and it bounds a
geographic coordinates ranging from 39º27'E to 39º30' E and
8º21'N and 8º46'30'' and the altitude of Adama Woreda ranges
from 1500 to 2300 meters above Mean Sea Level (MSL). The
population is moderately dense and annual rainfall is 500-800
mm. It is bounded by beset wereda in the east, Lome wereda in
the North West, Duga bora in south west, Dodo tana sire wereda
in south and Shenkora and minjar in north. The varied
topography which includes hills, plains and undulating
landscapes with lakes and the rift valley escarpment. The major
types of vegetation are bush scrub, grasslands. Only few places
are covered by little forest. location of lakes in study area has
given below.
Figure 1. Location of Lakes in AdamaWoreda
3. METHODOLOGY
The procedure performed in this project will derived the change
in Adama wereda and it’s surrounding over a different period of
time, the change detection in Adama wereda is carried out in the
three satellite images over different period of time (1984,2000
&2017). figure 2 below explains about entire flow of work.
3.1 Water Surface Extraction
In order to detect the changes in surface water area of Adama
woreda and its surrounding in the period 1984–2015, To analyze
the performance of different satellite derived indices including
Normalized Difference Water Index (NDWI), Modified
Normalized Difference Water Index (MNDWI), Modified, Water
Ratio Index (WRI), KT Transform and Supervised classification
has been examined for extraction of surface water from
individual temporal Landsat data
Table 1. Location of lakes around Adama woreda and its
surrounding
Figure 2. Methodology adopted for Study
NDWI, MNDWI, WRI, KT Transform and supervised
classification were needed to calculate from Landsat 1984
TM,2000TM & 2017OLI images to assess their performances for
the extraction of surface water. Threshold value for land-water
for each index has given manually to classify the image in to two
categories, water and non-water. (Komeil et al.,2014). suitable
threshold value has been identified by doing trial and error and
comparison to reference map generated using visual
interpretation. Near infrared (NIR) band is usually preferred for
visual interpretation of water bodies. Because NIR is strongly
absorbed by water and its reflected by vegetation and dry soil.
S.No Lake name Latitude Longitude
1 Chelekleka 8°45'39.64"N 38°58'8.58"E
2 BishoftuGuda 8°47'15.13"N 38°59'35.03"E
3 Kiroftu 8°46'45.63"N 39° 0'1.69"E
4 Hora 8°45'44.59"N 38°59'30.70"E
5 Bishoftu 8°44'36.03"N 38°59'2.62"E
6 Lake 1 8°46'45.81"N 39° 1'12.46"E
7 K’oftu 8°49'50.15"N 39° 2'55.37"E
8 Lake 2 8°48'12.13"N 39° 4'59.15"E
9 Koka 8°24'53.15"N 39° 6'30.51"E
10 Lake 3` 8°21'37.51"N 38°57'16.34"E
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume IV-5, 2018 ISPRS TC V Mid-term Symposium “Geospatial Technology – Pixel to People”, 20–23 November 2018, Dehradun, India
This contribution has been peer-reviewed. The double-blind peer-review was conducted on the basis of the full paper. https://doi.org/10.5194/isprs-annals-IV-5-181-2018 | © Authors 2018. CC BY 4.0 License.
182
Index Equation
Remark
Normalized
Difference in Water
Index
NDWI=
(Green−NIR)/
(Green + NIR)
Water has
positive value.
Wetness component of
K-T transformation
0.1509*Blue+ .1
793*Green+
0.3279* Red +
0.3406*NIR+
0.7112*MIR +
0.4572*SWIR
Water has
positive value
Modified Normalized
Difference Water
Index
MNDWI=
(Green−MIR)/
(Green + MIR)
Water has
positive value
Water Ratio Index
WRI =
(Green + Red)/
(NIR + MIR)
Value of water
body is greater
than 1
Table 2. Satellite-derived indexes used for water features
extraction in Landsat imagery
3.2 Water Ratio Index (WRI)
Since prevailing spectral reflectance of water in green (band-2)
and red (Band3) bands as compared to Near Infra-red (Band 4)
and Medium Infra-red (Band 5). Values of Water Ratio
Index(WRI) shows that greater than 1 for water (Shen L and Li
C,2010; Fang et al.,2011). where WRI is defined as
WRI=Green+Red/NIR+MIR
Fig 3 shows a Convincing result for water has been noticed in the
year of 2000 and 2017. But in 1984, Surface water falls under the
value ranges less than 1.
3.3 Normalized Difference Water Index (NDWI)
NDWI was introduced by McfeetersS.K. (1996) to extract
surface water from Landsat images depends on strong absorption
of water and strong reflection of vegetation in the portion of Near
infra-red regions.
NDWI=Green-NIR/ Green+NIR
Index value ranges from -1 to +1 with water body has high values
(very close to positive 1). Result proved that NDWI has ability to
separate water body and vegetation but it has some sort of
limitation on soil and built up area.
3.4 Modified Normalized Difference Water Index
(MNDWI)
In order to overcome the limitations of NDWI on urban areas, Xu
(2006) proposed new approach MNDWI which was found to be
more efficient in distinguishing water and urban areas. MNDWI
can be achieved with replacing infrared by shortwave Infrared
wavelength of 1.57-1.65 micro meters.
MNDWI=Green-MIR/GREEN+MIR
Some of the results has been observed while computing Modified
Normalized Difference water index are, 1) water has higher
positive values than in NDWI as it absorbs more MIR than
NIR.2). negative values indicate that built up land,3). still
vegetation and soil will have negative values as soil reflects MIR
more than NIR(Jensen,2004) and vegetation will reflect MIR
Figure 3. water ratio index of years 1984(a) ,2000(b)and 2017(c).
more than green.
Advantage of MNDWI is that more accurate in extraction of open
surface water body as built up land, soil and vegetation all
negative values and it was suppressed.
Figure 4. Modified Normalized difference in water indices of
1984(a) ,2000(b)and 2017(c).
3.5 Wetness component of K-T transformation
Tasseled cap transformation, also called K-T transformation, it
provides coordinate axis point to the direction has close by
association with features through rotating coordinate space. Once
rotation has performed, pointing direction of coordinate axis was
closely related to process of plant growth and soil (Yanan et al.,
2013). It helps to analyze and interpret the crop characteristics
and good practical significance. KT transform was introduced by
Kauth and Thomas (1996) to distinguish between three special
features as brightness, greenness and yellowness for MSS data.
Afterwards, Crist and Cicone(1984) utilized the tasseled cap
transformation for Thematic mapper data. The weights are
different and the third component is taken to represent soil
wetness rather than yellowness as in Kauth and Thomas' original
formulation. The wetness component of K-T transform can be
utilized to extract water information wherein a weighted sum of
bands of ETM+ is taken according to the following formula
(Vivek et al.,2015):
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume IV-5, 2018 ISPRS TC V Mid-term Symposium “Geospatial Technology – Pixel to People”, 20–23 November 2018, Dehradun, India
This contribution has been peer-reviewed. The double-blind peer-review was conducted on the basis of the full paper. https://doi.org/10.5194/isprs-annals-IV-5-181-2018 | © Authors 2018. CC BY 4.0 License.
183
Wetness=0.1509*Blue+0.1793*Green+0.3279*Red+0.3279*
Red+0.3406*NIR_0.7112*MIR_0.4572*MIR*0.4572*SWIR
Figure 5. wetness KT Transform of 1984(a), 2000(b) and
2017(c).
3.6 Supervised Classification
Supervised classification is the process of grouping pixels using
a known identity of specific sites (i.e., pixels already assigned to
informational classes) to classify pixels of unknown identity (i.e.,
to assign unclassified pixels to one of the several informational
classes (Cambell, 2002). Knowledge of study area, aerial
photography, and experience with remotely sensed data etc. are
required before undergoing training samples. The training
samples were provided over the image for known and
unambiguous representatives for each class namely Water body
and non-water. Maximum likelihood supervised classification
was utilized over the image.
4. RESULT AND DISCUSSION
4.1 Surface water changes from 1984-2000
Surface water area for each of the 10 lakes were calculated by
using Normalized Difference Water Index (NDWI) for 1984&
2000 (TM) and 2017 (OLI_TIRS). Figure 2 shows that Lakes
Chelekleka, BishoftuGuda, Kiroftu, Lake 1, K’oftu,Lake2, and
Koka has shown very little change in their shape & spatial
coverage for all years. Unlike lakes bishoftu, lake 2 and lake 3
has shown a very significant and discernible change in its surface
area cover for each study year. The surface area change detection
confirms that Lake BishoftuGuda, Kiroftu, Lake 1 and K'oftuhas
shown little change in their surface area over the study years.
Lake koka has seen its maximum surface area reduction of
3.51sqkm between 1984 and 2000. Surface water area increment
was evident in 2000 with Hora, Bishoftu, Lake 2 and Lake 3 were
0.041sqkm, 0.0087 sqkm,0.280sqkm,1.02sqkm respectively. The
overall surface area changes for the study area between 1984 and
2000was -3.94sqkms (negative sign indicating a decreasing
trend). +1.359 sq.km (positive sign indicates that increase in
trend).
4.2 Surface Water Changes from 2000-2017
Surface water area for each of the 10 lakes were calculated by
using Normalized Difference Water Index (NDWI) for 2000 and
Figure 6, a& b shows that current scenario of lake and c& d
shows changes from 2000-2017
Figure 7, a & b shows that current scenario of lake from Google
map and changes
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume IV-5, 2018 ISPRS TC V Mid-term Symposium “Geospatial Technology – Pixel to People”, 20–23 November 2018, Dehradun, India
This contribution has been peer-reviewed. The double-blind peer-review was conducted on the basis of the full paper. https://doi.org/10.5194/isprs-annals-IV-5-181-2018 | © Authors 2018. CC BY 4.0 License.
184
2017 (OLI_TIRS). Figure shows that Lakes Kiroftu, Lake 1 and
Lake 2has shown very little change in their shape & spatial
coverage for all years. Unlike lakes chelekleka and lake 3has
shown a very significant and discernible change in its surface
area cover for each study year. The surface area change detection
confirms that Lake Bishoftu Guda Kiroftu, Hora, K’oftu, Koka
has shown little change in their surface area over the study years.
Lake koka has seen its maximum surface area increase of14.88
sqkm between 2000 and 2017. apart from koka lake other lakes
namely Bishoftu Guda Kiroftu, Hora, K'oftu has increased as
compared with previous year in terms of area were
0.010sqkm,0.0091 sq km,0.020 sq km,0.04 sq.km. The overall
surface area changes for the study area between 2000 and 2017
was -4.98sqkms (negative sign indicating a decreasing trend).
+14.96401 sq km (positive sign indicates that increase in trend).
Figure
8, a& b shows that current scenario of lake and c& d shows
changes from 2000-2017
chelekleka Bishoftu Guda Kiroftu
year
Estimated
surface
area
(sq km)
surface
area
changes
from
previous
year
Estimated
surface
area
(sq km)
surface
area
changes
from
previous
year
Estimated
surface
area
(sq km)
surface
area
changes
from
previous
year
1984 1.611 0.707 0.200
2000 1.250 -0.361 0.704 -0.002 0.194 -0.006
2017 0.015 -1.235 0.715 0.010 0.158 -0.036
Table 3. Surface water changes during the years 1984- 2017
Figure 9, a &b shows current scenario of lakes and changes in
koka lake from 2000-2017
Hora Bishoftu No Name 1
year
Estimated
surface area
(sq-km) surface
area
changes
from
previous
year
Estimated
surface area
(sq km) surface
area
changes
from
previous
year
Estimated
surface area
(sq km) surface
area
changes
from
previous
year
1984 1.067 0.933 0.083
2000 1.109 0.042 0.942 0.009 0.038 -0.046
2017 1.118 0.009 0.963 0.021 0.000 -0.038
Table 4. Surface water changes during the years 1984- 2017
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume IV-5, 2018 ISPRS TC V Mid-term Symposium “Geospatial Technology – Pixel to People”, 20–23 November 2018, Dehradun, India
This contribution has been peer-reviewed. The double-blind peer-review was conducted on the basis of the full paper. https://doi.org/10.5194/isprs-annals-IV-5-181-2018 | © Authors 2018. CC BY 4.0 License.
185
K'oftu No Name 2 Koka No Name 3
year
Estimate
d
surface
area
(sq km)
surface
area
change
s
from
previou
s year
Estimate
d
surface
area
(sq km)
surface
area
change
s
from
previou
s year
Estimate
d
surface
area
(sq km)
surface
area
change
s
from
previou
s year
Estimate
d
surface
area
(sq km)
surface
area
change
s
from
previou
s year
198
4 1.368 0.785 139.151 2.572
200
0 1.357 -0.010 1.065 0.281 135.636 -3.516 3.601 1.028
201
7 1.400 0.043 0.876 -0.189 150.516 14.881 0.112 -3.489
Table 5. Surface water changes during the years 1984- 2017
Figure 10. Area of surface water during 1984-2017
Figure 11. Area of surface water during 1984-2017
Figure 12. Area of surface water during 1984-2017
Figure 13. Area of surface water in percentage during (1984-
2000).
Figure 14. Area of surface water in percentage during (2000-
2017)
(22.397)
(2.986)
3.891 0.934
(54.843)
(0.746)
35.782
(2.527)
39.980
-60
-40
-20
0
20
40
60
1984-2000
0
0.5
1
1.5
2
2.5
3
3.5
4
Lake 1 K'oftu Lake 2 Lake 3
Are
a in
sq
km
Lake Name
1984
2000
2017
125
130
135
140
145
150
155
Koka
Are
a in
Sq
.Km
Lake Name
1984
2000
2017
(76.659)
(18.127)
0.857 2.248
(45.157)
3.128
(24.099)
0.015
(96.895)
-113
-93
-73
-53
-33
-13
7
2000-2017
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume IV-5, 2018 ISPRS TC V Mid-term Symposium “Geospatial Technology – Pixel to People”, 20–23 November 2018, Dehradun, India
This contribution has been peer-reviewed. The double-blind peer-review was conducted on the basis of the full paper. https://doi.org/10.5194/isprs-annals-IV-5-181-2018 | © Authors 2018. CC BY 4.0 License.
186
5. CONCLUSION
This study aimed to model the spatio-temporal changes of Lakes
in Adama woreda and its surrounding in the period 1984-2017.
Through a comparative analysis, the NDWI was selected and
employed for this purpose.
The results showed an intense decreasing trend in the
lakes of chelekleka, kiroftu, lake 1 and lake 3 of surface area in
the period 1984–2017, especially between 2000 and 2017 when
the lake lost about 1.309 km2 (one third) of its surface area
compared to the year 2000, which is equivalent to 76%,18%
,0.03% and 96%. Interestingly koka lake has shown very erratic
changes in its area coverage by losing almost 3.5 sq.km between
1984 and 2000 and then climbing back up by 14.8 sq.km in 2017.
Percentage of increment was observed that 10.6 % as compared
with previous year. This, makes Lakes in Adama woreda and its
surrounding with a very dramatic decline and rise history in about
thirty-three years’ period. If such a decreasing trend in Lakes in
Adama woreda continues, it is very likely that the lake will lose
its entire water surface in the near future
REFERENCES
Salmon, B.P., Kleynhans, W.,van Den Bergh, F., Olivier, J.C.,
Grobler, T.L,; Wessels, K.J., 2013. Land cover change detection
using the internal covariance matrix of the extended Kalman
filter over multiple spectral bands, IEEE J. Sel. Topics Appl.
Earth Observations Remote Sens, Vol. VI, pp. 1079–1085.
Demir, B., Bovolo, F., Bruzzone, L, 2013.Updating land-cover
maps by classification of image time series: A novel change-
detection-driven transfer learning approach,IEEE Trans. Geosci.
Remote Sens, Vol.51, pp. 300–312.
Volpi, M., Petropoulos, G.P., Kanevski, M, 2013. Flooding
extent cartography with Landsat TM imagery and regularized
Kernel Fisher’s discriminant analysis,Comput. Geosci
,Vol.57,pp. 24–31.
Brisco, B., Schmitt, A., Murnaghan, K., Kaya, S., Roth,
A,2013.Sar polarimetric change detection for flooded
vegetation,. Int. J. Digit. Earth,Vol. VI, pp. 103–114.
Kaliraj, S., Muthu Meenakshi, S., Malar, V.K,2012.Application
of remote sensing in detection of forest cover changes using geo-
statistical change detection matrices—A case study of
devanampatti reserve forest, tamilnadu, India,Nat. Environ.
Polluti. Technol,Vol. XI, pp. 261–269.
Markogianni, V., Dimitriou, E., Kalivas, D.P,2013.Land-use and
vegetation change detection in plastira artificial lake catchment
(Greece) by using remote-sensing and GIS techniques, Int. J.
Remote Sens,Vol. XXXIV, pp. 1265–1281.
Bagan, H., Yamagata, Y.,2012.. Landsat analysis of urban
growth: How Tokyo became the world’s largest megacity during
the last 40 years,. Remote Sens. Environ,Vol. CXXVII, pp. 210–
222.
Raja, R.A.A., Anand, V., Kumar, A.S., Maithani, S., Kumar,
V.A.,2013.Wavelet based post classification change detection
technique for urban growth monitoring, J. Indian Soc. Remote
Sens,Vol. XLI, pp. 35–43.
Dronova, I., Gong, P., Wang, L,2011.Object-based analysis and
change detection of major wetland cover types and their
classification uncertainty during the low water period at Poyang
Lake, China,Remote Sens. Environ,Vol. CXV, pp.3220-3236.
Zhu, X., Cao, J., Dai, Y,2011.A Decision Tree Model For
Meteorological Disasters Grade Evaluation of Flood,In
Proceedings of 4th International Joint Conference on
Computational Sciences and Optimization 2011, Kunming and
Lijiang, Yunnan, China, 15–19,pp. 916–919.
Ridd, M.K., Liu, J,1998.A comparison of four algorithms for
change detection in an urban environment,Remote Sens.
Environ,Vol. LXIII, pp.95-100.
Lu, S., Wu, B., Yan, N., Wang, H,2011.Water body mapping
method with HJ-1A/B satellite imagery, Int. J. Appl. Earth Obs.
Geoinf,Vol.XIII, pp.428-434.
Desmet, P.J.J., Govers, G,1996. A GIS procedure for
automatically calculating the USLE LS factor on topographically
complex landscape units,J. Soil Water Conserv,Vol.LI, pp.427-
433.
Zhou, W., Wu, B,2008. Assessment of soil erosion and sediment
delivery ratio using remote sensing and GIS: A case study of
upstream chaobaihe river catchment, north China, Int. J.
Sediment Res,Vol.XXII, pp.167-173.
Du, Z., Linghu, B., Ling, F., Li, W., Tian, W., Wang, H., Gui, Y.,
Sun, B., Zhang, X,2012.Estimating surface water area changes
using time-series Landsat data in the qingjiang river
basin,China.J.Appl.RemoteSens,Vol.VI,doi:10.1117/1.JRS.6.06
3609
Sun, F., Sun, W., Chen, J.,Gong, P,2012.Comparison and
improvement of methods for identifying waterbodies in remotely
sensed imagery, Int. J. Remote Sens,Vol.XXXIII, pp.6854-6875.
Water Body Extraction from Multi-Source Satellite Images.
Available online:
http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.301.8
.33&rep=rep1&type=pdf (accessed on 21 June 2003).
Xu, H,2006. Modification of normalised difference water index
(NDWI) to enhance open water features in remotely sensed
imagery,Int. J. Remote Sens,Vol.XXVII, pp.3025-3033.
Water Body Extraction And Change Detection Based on Multi-
Temporal SAR Images. Available online:
http://adsabs.harvard.edu/abs/2009SPIE.7498E..96Z (accessed
on 21 January 2014).
Zhou, H., Hong, J., Huang, Q,2011.Landscape and water quality
change detection in urban wetland: A post-classification
comparison method with IKONOS data,Procedia Environ.
Sci,Vol.X, pp.1726-1731.
Tang, Z., Ou, W., Dai, Y., Xin, Y,2013. Extraction of water body
based on Landsat TM5 imagery—A case study in the Yangtze
river, Adv. Inf. Comm. Technoly,Vol.CCCXCIII, pp.416-420.
Li, W., Du, Z., Ling, F., Zhou, D., Wang, H., Gui, Y., Sun, B.,
Zhang, X,2013.A comparison of land surface water mapping
using the normalized difference water index from TM, ETM+
and ALI,Remote Sens,Vol.V, pp.5530-5549.
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume IV-5, 2018 ISPRS TC V Mid-term Symposium “Geospatial Technology – Pixel to People”, 20–23 November 2018, Dehradun, India
This contribution has been peer-reviewed. The double-blind peer-review was conducted on the basis of the full paper. https://doi.org/10.5194/isprs-annals-IV-5-181-2018 | © Authors 2018. CC BY 4.0 License.
187
McFeeters, S.K,2013. Using the normalized difference water
index (NDWI) within a geographic information system to detect
swimming pools for mosquito abatement: A practical
approach,Remote Sens,Vol.V, pp.3544-3561.
Moradi.M ,Sahebi.M , Shokri.M,2017.Modified Optimization
Water Index (Mowi) For Landsat-8 Oli/Tirs,The International
Archives of the Photogrammetry, Remote Sensing and Spatial
Information Sciences,Volume XLII-4/W4, 2017 Tehran's Joint
ISPRS Conferences of GI Research, SMPR and EOEC 2017, 7–
10 October 2017, Tehran, Iran.
McFeeters, S. K,1996. The use of the Normalized Difference
Water Index (NDWI) in the delineation of open water
features,International journal of remote sensing,Vol.XVII,
pp.1425-1432.
Xu, H, 2006.Modification of normalised difference water index
(NDWI) to enhance open water features in remotely sensed
imagery,International journal of remote sensing,Vol.XXVII,
pp.3025-3033.
Irons, J. R., J. L. Dwyer ,J. A. Barsi,2012.The next Landsat
satellite: The Landsat data continuity mission,Remote Sensing of
Environment,Vol.CXXII, pp.11-21.
Tulbure, M. G, M. Broich, 2013. Spatiotemporal dynamic of
surface water bodies using Landsat time-series data from 1999 to
2011,.ISPRS Journal of Photogrammetry and Remote
Sensing,Vol.LXXIX, pp.44-52.
Ko, B. C., H. H. Kim,J. Y. Nam,2015.Classification of potential
water bodies using Landsat 8 OLI and a combination of two
boosted random forest classifiers,Sensors,Vol.XV, pp.13763-
13777.
Sethre, P. R., B. C. Rundquist , P. E. Todhunter,2005.Remote
detection of prairie pothole ponds in the Devils Lake Basin,
North Dakota,GIScience & Remote Sensing,Vol.XLII, pp.277-
296
Klein, I., A. J. Dietz, U. Gessner, A. Galayeva, A. Myrzakhmetov
.,C. Kuenzer,2014.Evaluation of seasonal water body extents in
Central Asia over the past 27 years derived from medium-
resolution remote sensing data,International Journal of Applied
Earth Observation and Geoinformation,Vol.XXVI, pp.335-349.
Ji, L., L. Zhang., B. Wylie,2009.Analysis of dynamic thresholds
for the normalized difference water index, Photogrammetric
Engineering & Remote Sensing,Vol.LXXV, pp.1307-1317.
Du, Z., Linghu, B,. Ling, F., Li, W., Tian, W., Wang, H., Gui, Y.,
Sun, B., Zhang, X,2012.Estimating surface water area changes
using time-series Landsat data in the qingjiang river basin,China.
J. Appl. Remote Sens,doi:10.1117/1.JRS.6.063609.
McFeeters, S.K,1996.The use of the normalized difference water
index (NDWI) in the delineation of open water features,. Int. J.
Remote Sens,Vol.XVII, pp.1425-1432.
Li, W., Z. Du, F. Ling, D. Zhou, H. Wang, Y. Gui, B. Sun.,
X.Zhang,2013.A comparison of land surface water
mappingusing the normalized difference water index from TM,
ETM+and ALI,Remote Sensing,Vol.V, pp.5530-5549.
Shen, L .and C. Li,2010.Water body extraction from Landsat
ETM+ imagery using adaboost algorithm,Geoinformatics, 2010
18th International Conference on, IEEE.
Komeil Rokni., Anuar Ahmad ., Ali Selamat., Sharifeh
Hazini,2014.Water Feature Extraction and Change Detection
Using Multi temporal Landsat Imagery,Remote Sens,Vol.VI,
pp.4173-4189,doi:10.3390/rs6054173.
Shen L., Li C,2010.Water Body Extraction from Landsat ETM+
Imagery using Adaboost algorithm,Proceedings of 18th
International Conference on Geoinformatics, Beijing, China,pp.
1-4.
Fang-fang Z., Bing Z., Jun-sheng L., Qian, S., Yuan-feng W.,
Yang S.,2011.Comparative analysis of automatic water
identification method based on multispectral Remote
Sensing,Procedia Environmental Sciences,Vol.XI, pp.1482-
1487.
Jensen, J.R.,2004.Introductory digital image processing: A
remote sensing perspective, 3rd edition (NJ: Prentice Hall
Logicon Geodynamics, Inc).
Yanan Wang ., Fang Huang., Yuchun Wei.,2013.Water Body
Extraction from LANDSAT ETM+ Image Using MNDWI and
K-T Transformation, 21st International Conference on
Geoinformatics (GEOINFORMATICS), Issue Date: 20-22 June
2013.
Vivek Kumar Gautama, Piyush Kumar Gaurava , P Murugana ,
M Annadurai.,Assessment of Surface Water Dynamicsin
Bangalore using WRI, NDWI, MNDWI,Supervised
Classification and K-T Transformation.International Conference
On Water Resources, Coastal And Ocean Engineering (Icwrcoe
2015).
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume IV-5, 2018 ISPRS TC V Mid-term Symposium “Geospatial Technology – Pixel to People”, 20–23 November 2018, Dehradun, India
This contribution has been peer-reviewed. The double-blind peer-review was conducted on the basis of the full paper. https://doi.org/10.5194/isprs-annals-IV-5-181-2018 | © Authors 2018. CC BY 4.0 License.
188